Hyperspectral Retinal Observations for the Cross-sectional Detection of Alzheimer's Disease
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 80
- 试验地点
- 4
- 主要终点
- CoGNIT test diagnostic accuracy
研究概览
简要总结
Two devices will be tested in this research:
- Mantis Photonics' hyperspectral camera for non-invasive retinal examination (i.e., a hardware medical device under investigation).
- Blekinge CoGNIT cognitive ability test (i.e., an assessment).
详细描述
Worldwide, millions of people are affected by neurodegenerative diseases (e.g., Alzheimer's disease, dementia). Those diseases are having a tremendous socio-economic impact on our society. The cost associated with treating and caring for those diseases is enormous. Overwhelming evidence indicates how selective lifestyle changes (e.g., reducing exposure to known risk factors) can sometimes significantly decrease the probability of developing the disease or delay its onset. However, the diseases must be diagnosed early for them to be effective. There is a lack of accessible, inexpensive, and non-invasive practices that would allow for an early diagnosis of different diseases, even at the primary physician's office. Mantis Photonics and Blekinge Tekniska Högskola (Institustionen för Hälsa) aim to fill this urgent unmet medical need.
Strong indications of the possibility of classifying Alzheimer's status based on hyperspectral scans of the retina have been published by different researchers. These results were obtained based on images taken with hyperspectral cameras with a different working principle than the Mantis Photonics camera. The working principle of the Mantis Photonics camera allows making a hyperspectral retinoscopy with the same spectral range and comparable or better spectral resolution with a machine that is more modular and lower in cost. There is thus reason to hypothesize retinal scans taken with the Mantis Photonics camera can be used for the same classification task.
Previous studies on the automated tablet computer cognitive test CoGNIT have established validity, reliability and sensitivity for testing patients with Normal Pressure Hydrocephalus (NPH) . Recently feasibility of testing in Mild Cognitive Impairment (MCI) was affirmed (Behrens, Berglund, & Anderberg, CoGNIT Automated Tablet Computer Cognitive Testing in Patients With Mild Cognitive Impairment: Feasibility Study, 2022). In NPH patients, CoGNIT was more sensitive to cognitive impairment at baseline and cognitive improvement after shunt surgery than the Mini-Mental State Examination (MMSE).
Blood tests for amyloid-β and other biomarkers related to Alzheimer's disease are being investigated for clinical practice, but the technique is not accepted as a standard test. Research has shown that renal function influences amyloid-β clearance from the body. Also, analytical errors influence test results. Therefore, one can question the influence of normal repeatability of the blood test result.
The aim of this investigation is the evaluation, (further) development and comparison of non-invasive techniques for the evaluation of patients suffering mild cognitive impairment, in particular, the Mantis Photonics hyperspectral camera with classification machine learning model in combination with the CoGNIT test of Dr Behrens (Blekinge Tekniska Högskola). These techniques will be compared to the result of cerebrospinal fluid analysis (CSF), the reference biological diagnostic technique for Alzheimer's disease.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
盲法说明
The diagnosis of Amyloidosis (biomarker of Alzheimer's disease) is made based on the normal patient care consisting of the neurologist assessment and the Cerebro-Spinal Fluid analysis.
This diagnosis is used as golden standard for the model based on retinal images and cognitive test results.
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •subject age over 18 years old
- •The subject has undergone a lumbar puncture an cerebrospinal fluid analysis as part of the standard care.
- •The subject has at least one healthy eye.
- •The subject is applicable for taking a blood sample for the blood analysis test.
- •The informed consent is provided, explained and understood by the person. The person has consented to the informed consent.
排除标准
- •There are contra-indications for lumbar puncture (eg: brain tumor with suspicion of raised intracranial pressure, coagulopathies or ongoing anticoagulant medications) will be excluded from the study.
- •When the subject suffers from excessive visual or auditive impairment, the he/she will be excluded from the CoGNIT track.
结局指标
主要结局
CoGNIT test diagnostic accuracy
时间窗: within 2 months after last patient procedure
Accuracy \[percent\] of diagnosis based on the CoGNIT test data
Accuracy (Statistical metric) retinal image classification model
时间窗: within 2 months after last patient procedure
Performance metric of the retinal image classification model: model accuracy \[percent\]
Area under the Curve (statistical metrics) retinal image classification model
时间窗: within 2 months after last patient procedure
Performance metric of the retinal image classification model: Area under the Curve (AuC) \[0 \< AuC \< 1\]
Sensitivity (Statistical metric) retinal image classification model
时间窗: within 2 months after last patient procedure
Performance metrics of the retinal image classification model: Sensitivity \[percent\]
次要结局
- Non invasive test variability compared to reference(within 3 months after last patient procedure)
- Accuracy: Metrics combination model(within 3 months after last patient procedure)
- Sensitivity: Metrics combination model(within 3 months after last patient procedure)
- Area Under the Curve: Metrics combination model(within 3 months after last patient procedure)